Declan Conway and Patrick Curran use recent findings from research into different risk assessment approaches and adaptation planning in climate change ‘hotspots’ in Africa and Asia to show the value of hybrid methods for creating more sustainable adaptation strategies.

The objectives of mitigating climate change – addressing its root causes by reducing emissions of, or locking-up, greenhouse gases – are well understood, albeit highly challenging. In contrast, understanding how to adapt to a climate that is already warming is not straightforward.

There are some certainties: that the poorest and most vulnerable will be most affected; that adaptation strategies need to be put in place now to manage existing impacts and will be increasingly vital as the world warms further. But many complex questions remain: How will future climate risks manifest? Who should be involved in supporting communities to adapt? Will incremental changes be sufficient or will people need to completely transform their way of life?

To date there has been a dichotomy between top-down and bottom-up approaches to understanding climate risks and how to manage them. While both approaches grapple with how to present the knowledge generated and how to translate it to apply to complex human–environment systems, there are challenges unique to each and in the differences between them. But now a ‘Perspective’ evaluating work carried out under the Collaborative Adaptation Research Initiative in Africa and Asia (CARIAA) project offers some insights on how best to combine the strengths of each approach.

What defines current approaches to risk assessment and adaptation planning?

In general, top-down risk assessments involve producing quantitative descriptions of the long-term future climate from global climate models (GCMs). These projections are used as a starting point to assess the physical and ecological risks and impacts of climate change at different temperature stabilisation levels (say 1.5°C or 2.0°C) – for example, vulnerability to sea-level rise, extent of flood plain inundation, changing rainfall patterns. The identified risks in turn inform decisions on their minimisation, avoidance or management, which are then translated into adaptation strategies.

Top-down approaches are most frequently applied to define initial assumptions and to scope adaptation assessments. However, their high demands for technical capacity can be a challenge, as can translating the broad geographical scale of impacts, in order to generate locally relevant knowledge about future risks. Top-down approaches are also often criticised for their lack of critical engagement with underlying physical or socioeconomic factors such as political processes.

In contrast, as the Perspective published in Nature Climate Change highlights, bottom-up assessments tend to comprise building an understanding at finer geographical scales and focus on physical, ecological or social processes and their sensitivity to weather and climate. They are people-centred and attempt to derive and generate knowledge based on people’s past and present understandings of changing conditions, how they assess risks, what impacts these have, and the responses they adopt. Thus these approaches are generally applied to understand specific contexts and how impacts are transmitted through the socioeconomic organisation of communities.

Bottom-up approaches also come with challenges, such as the resource-intensive nature of conducting contextual studies, being able to produce sufficient detail about complex systems on which to base decisions, and difficulties associated with generalising findings or lessons to other contexts.

Ultimately the main contrast between the approaches lies in the fact that while bottom-up methods embrace the complexity of lived experiences through understanding the past and present conditions, top-down approaches aim to simplify complex systems to simulate the climate signal (changes lying outside natural variability) and understand future changes.

Learning from three climate-sensitive ‘hotspots’ to advance linkages between approaches

In all the hotspots, using the top-down approach, temperature projections suggest that warming is likely to be higher than the global mean and that there are quantifiable differences in precipitation, albeit to a lesser extent. However, the projections do not simulate the sectoral or livelihood impacts of climate change; these are merely implied and come with high levels of uncertainty. As such, while useful for some elements of planning, they do not provide the full context of the socioeconomic system in which the impacts will occur, or provide enough information to enable people to make effective decisions to manage the risks.

Meanwhile, when a rich diversity of bottom-up approaches were used, including surveys, statistical modelling, detailed life histories and focus group discussions with communities, they provided contextualised insights into the consequences of complex climate impacts. These insights provide a picture of how communities could respond to future warming levels and are often of interest to stakeholders and decision-makers.

The bottom-up approaches generated information on the broader socioeconomic factors that influence individual or community responses to change, such as migration, business decisions and livelihood choices. The climate signal that affects these actions manifests in complex ways, and often it is not possible to clearly attribute climate impacts on behaviour because of confounding factors. However, the research does consistently find that climatic risks do mediate and influence many responses in some form.

The studies carried out across deltas (including the Ganges-Brahmaputra, Indian Mahanadi and African Volta) indicate that socially marginalised populations have a very limited range of adaptation options open to them, and that established migration flows, which are one way people act to avoid or limit climate risk, are themselves sensitive to climatic changes. Being mobile – to commute to carry out work or to relocate to a location with lesser risks – was also shown to be an essential feature of many livelihoods (such as pastoralism, farming and natural-resource-based trading) in the semi-arid study regions within Ghana, Kenya, Namibia and India.

Overall, the bottom-up studies provide valuable insights into vulnerabilities within societies that have already experienced climate change impacts locally. Pairing this information with the top-down projections in climate hotspots is useful and practical for adaptation planning.

Supporting effective adaptation strategies at future temperature stabilisation levels requires both approaches

Understanding how impacts are likely to differ at different temperature levels (e.g. with a rise of 1.5°C or 2°C above pre-industrial levels) has primarily been achieved through top-down modelling approaches to date. To obtain a more detailed picture and to support effective adaptation at any level of temperature stabilisation, contributions from bottom-up approaches will be needed too.

To make much-needed progress in this direction, more engagement is required between risk assessors using the two different approaches. Although top-down approaches tend to focus on the longer-term future and bottom-up approaches on the recent past and present, that distinction is not exclusive. Bottom-up knowledge of complex human–environment dynamics has already informed computational models of actions and interactions of individuals and groups (‘agent-based modelling’) for simulations of the future, but this needs to happen more.

As the Nature Climate Change paper highlights, the CARIAA case studies show in particular the clear need for an iterative process. Such an approach would see the results from top-down studies feeding into the bottom-up approaches, the outputs of which could then be used to inform and increase the skill of the top-down methods. Blending insights from bottom-up and top-down can generate a more holistic picture of the fuller range of vulnerabilities of people and communities to climate risks, and their potential responses, including physical, social and economic. There needs to be a continual process through which the two approaches inform each other, conceptually and practically.

Achieving this deep understanding, particularly through close partnerships between modellers, policymakers, communities and private sector representatives, is more likely to reveal local priorities and measures that can be well integrated into existing local processes, and that underpin sustainable adaptation at projected future temperatures. Bottom-up studies of adaptation are important for policy development – they provide examples of what works and what does not. In turn, these can guide the information required from top-down approaches, including its presentation and accessibility.

A role for knowledge brokers is central to this process as it relies on knowledge synthesis and communication to inform practical actions, including in the present. What is commonly highlighted through the CARIAA case studies is that any assessment of risks is most useful if it can be used in present-day decision-making.

Finally, while the urgent need to promote effective adaptation strategies is driving a fertile research agenda, this research needs to be more interdisciplinary, not least across the physical, natural and social sciences. The onus is on researchers to create these new partnerships, including through engagement with policy processes and practitioners, in a bid to design and map out pathways and options that are useful to inform decisions using both top-down and bottom-up methods.

Generating hybrid methods and information is likely to be of greater utility than using a single approach, in both the short and long-term, for decision-makers and communities alike.

‘The need for bottom-up assessments of climate risks and adaptation in climate-sensitive regions’ by Declan Conway et al. was published in Nature Climate Change on 17 June 2019. The research is part of the Pathways to Resilience (PRISE) project.

The views in this commentary are those of the authors and do not necessarily represent those of the Grantham Research Institute.

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